AI-Human Co-Working Models for City-Scale Energy-Saving Transformation in Existing Building Stock

The decarbonisation of the built environment presents a major challenge for local governments, particularly in cities with large, aging housing stocks and diverse community needs. Traditional approaches to building retrofit are resource-intensive, slow, and often fail to engage residents meaningfully or at scale. While data-driven methods and behavioural interventions have been individually explored, there is a critical gap in integrated, city-scale solutions that can simultaneously identify retrofit potential and empower communities to act on it.

This project aims to develop a novel AI-Human co-working model that enables scalable and participatory energy-saving transformations across large numbers of existing buildings in urban settings. The model will combine AI-powered building and occupant analysis with human-centric communication and co-design methods to unlock both technical and behavioural pathways to carbon reduction.

Using publicly available datasets (e.g. LiDAR, EPCs, housing stock records) and city-specific data from partners such as local authorities and housing associations, the project will automate the identification of building-level energy-saving opportunities by analysing physical characteristics, usage patterns, and retrofit needs. In parallel, the project will develop AI-assisted, personalised communication strategies to engage occupants and communities at scale, encouraging low-carbon behaviour change and participatory retrofit planning.

Through case studies in council-owned and private housing, the project will evaluate the effectiveness of co-designed interventions, measure actual versus predicted energy savings, and assess alignment between retrofit measures and occupant behaviour. The ultimate goal is to optimise the allocation of funding and resources by targeting interventions that offer the highest impact and uptake potential, while fostering long-term community ownership.

This interdisciplinary research will contribute to the fields of smart cities, AI for sustainability, and behavioural energy science, offering local authorities a scalable roadmap to drive practical, inclusive, and cost-effective decarbonisation strategies.